Parallel Smith-Waterman Algorithm for Gene Sequencing

Smith-Waterman Algorithm represents a highly robust and efficient parallel computing system development for biological gene sequence. The research work here gives a deep understanding and knowledge transfer about exiting approach for gene sequencing and alignment using Smith-waterman their strength and weaknesses. Smith-Waterman algorithm calculates the local alignment of two given sequences used to identify similar RNA, DNA and protein segments. To identify the enhanced local alignments of biological gene pairs Smith-Waterman algorithm uses dynamic programming approach. It is proficient in finding the optimal local alignment considering the given scoring system. DOI: 10.17762/ijritcc2321-8169.150515

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists

High performance reconfigurable architectures for biological sequence alignment

Bioinformatics and computational biology (BCB) is a rapidly developing multidisciplinary field which encompasses a wide range of domains, including genomic sequence alignments. It is a fundamental tool in molecular biology in searching for homology between sequences. Sequence alignments are currently gaining close attention due to their great impact on the quality aspects of life such as facilitating early disease diagnosis, identifying the characteristics of a newly discovered sequence, and drug engineering. With the vast growth of genomic data, searching for a sequence homology over huge databases (often measured in gigabytes) is unable to produce results within a realistic time, hence the need for acceleration. Since the exponential increase of biological databases as a result of the human genome project (HGP), supercomputers and other parallel architectures such as the special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms. Nevertheless, there are always trade-off between area, speed, power, cost, development time and reusability when selecting an acceleration platform. FPGAs generally offer more flexibility, higher performance and lower overheads. However, they suffer from a relatively low level programming model as compared with off-the-shelf microprocessors such as standard microprocessors and GPUs. Due to the aforementioned limitations, the need has arisen for optimized FPGA core implementations which are crucial for this technology to become viable in high performance computing (HPC). This research proposes the use of state-of-the-art reprogrammable system-on-chip technology on FPGAs to accelerate three widely-used sequence alignment algorithms; the Smith-Waterman with affine gap penalty algorithm, the profile hidden Markov model (HMM) algorithm and the Basic Local Alignment Search Tool (BLAST) algorithm. The three novel aspects of this research are firstly that the algorithms are designed and implemented in hardware, with each core achieving the highest performance compared to the state-of-the-art. Secondly, an efficient scheduling strategy based on the double buffering technique is adopted into the hardware architectures. Here, when the alignment matrix computation task is overlapped with the PE configuration in a folded systolic array, the overall throughput of the core is significantly increased. This is due to the bound PE configuration time and the parallel PE configuration approach irrespective of the number of PEs in a systolic array. In addition, the use of only two configuration elements in the PE optimizes hardware resources and enables the scalability of PE systolic arrays without relying on restricted onboard memory resources. Finally, a new performance metric is devised, which facilitates the effective comparison of design performance between different FPGA devices and families. The normalized performance indicator (speed-up per area per process technology) takes out advantages of the area and lithography technology of any FPGA resulting in fairer comparisons. The cores have been designed using Verilog HDL and prototyped on the Alpha Data ADM-XRC-5LX card with the Virtex-5 XC5VLX110-3FF1153 FPGA. The implementation results show that the proposed architectures achieved giga cell updates per second (GCUPS) performances of 26.8, 29.5 and 24.2 respectively for the acceleration of the Smith-Waterman with affine gap penalty algorithm, the profile HMM algorithm and the BLAST algorithm. In terms of speed-up improvements, comparisons were made on performance of the designed cores against their corresponding software and the reported FPGA implementations. In the case of comparison with equivalent software execution, acceleration of the optimal alignment algorithm in hardware yielded an average speed-up of 269x as compared to the SSEARCH 35 software. For the profile HMM-based sequence alignment, the designed core achieved speed-up of 103x and 8.3x against the HMMER 2.0 and the latest version of HMMER (version 3.0) respectively. On the other hand, the implementation of the gapped BLAST with the two-hit method in hardware achieved a greater than tenfold speed-up compared to the latest NCBI BLAST software. In terms of comparison against other reported FPGA implementations, the proposed normalized performance indicator was used to evaluate the designed architectures fairly. The results showed that the first architecture achieved more than 50 percent improvement, while acceleration of the profile HMM sequence alignment in hardware gained a normalized speed-up of 1.34. In the case of the gapped BLAST with the two-hit method, the designed core achieved 11x speed-up after taking out advantages of the Virtex-5 FPGA. In addition, further analysis was conducted in terms of cost and power performances; it was noted that, the core achieved 0.46 MCUPS per dollar spent and 958.1 MCUPS per watt. This shows that FPGAs can be an attractive platform for high performance computation with advantages of smaller area footprint as well as represent economic ‘green’ solution compared to the other acceleration platforms. Higher throughput can be achieved by redeploying the cores on newer, bigger and faster FPGAs with minimal design effort

Diseño e implementación de sistemas de computación de alto rendimiento para acelerar algoritmos biomédicos

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura : 26-06-201

Comparação paralela exata de sequências biológicas em plataformas híbridas de alto desempenho

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Programa de Pós-Graduação em Informática, 2013.Quando uma nova sequência biológica é descoberta, suas características funcionais e estruturais devem ser estabelecidas. Para isso, a sequência é comparada com outras sequências, procurando por similaridades. A comparação de sequências é, então, uma das operações básicas em Bioinformática. O algoritmo mais preciso para executar compara- ções é o proposto por Smith-Waterman (SW), que é baseado em programação dinâmica e possui complexidade quadrática de tempo e espaço. Essa complexidade pode facilmente levar a um alto tempo de execução e uso de memória. Técnicas de processamento paralelo podem ser utilizadas para produzir resultados em menos tempo. Existem muitas versões paralelas do algoritmo SW na literatura que se executam em multicores, GPUs, FPGAs e CellBEs. Mesmo que existam algumas abordagens que executem o algoritmo SW em plataformas híbridas compostas por GPUs e multicores, elas alocam trabalho de forma xa, baseada no desempenho teórico das unidades de processamento ou nos resultados obtidos por benchmarks. Essa dissertação de Mestrado propõe e avalia uma estratégia otimizada e exível para executar o algoritmo SW em plataformas híbridas compostas por GPUs e multicores com extensões SIMD. A nossa estratégia fornece múltiplas polí- ticas de alocação de tarefas e o usuário pode escolher a que é mais apropriada para o seu problema. Propomos também um mecanismo de re-trabalho que trata situações que ocorrem quando nodos mais lentos recebem as últimas e maiores tarefas. Os resultados obtidos comparando sequências de busca com cinco diferentes bancos de dados genômicos em uma plataforma composta por 4 GPUs e 2 multicores mostram que a nossa aborda- gem é capaz de reduzir o tempo de execução em plataformas híbridas, quando comparada com soluções que utilizam apenas GPUs. Mostramos também que o nosso mecanismo de re-trabalho pode melhorar signi cativamente o desempenho na plataforma utilizada. ______________________________________________________________________________ ABSTRACTOnce a new biological sequence is discovered, its functional and structural characteris- tics must be established. In order to do that, the newly discovered sequence is compared against other sequences, looking for similarities. Sequence comparison is, therefore, one of the most basic operations in Bioinformatics. The most accurate algorithm to execute pairwise comparisons is the one proposed by Smith-Waterman (SW), which is based on dynamic programming, with quadratic time and space complexity. This can easily lead to very high execution times and huge memory requirements. Parallel processing can be used to produce results faster, reducing signi cantly the time needed to obtain results with the SW algorithm. There are many parallel versions of SW in the literature, which run in multicores, GPUs, Field-Programmable Gate Arrays (FPGAs) and CellBEs. Even though there are some versions of SW that run on hybrid platforms composed of GPUs and multicores, they assign work in a xed way, based on the theoretical performance of the processing units or in the results obtained by some benchmarks. This MsC Disser-tation proposes and evaluates a exible and optimized strategy to run Smith-Waterman applications in hybrid platforms composed of GPUs and multicores with SIMD extensions. Our strategy provides multiple task allocation policies and the user can choose the one which is more appropriate to his/her problem. We also propose a workload adjustment mechanism that tackles situations that arise when slow nodes receive the last tasks. The results obtained comparing query sequences to 5 public genomic databases in a platform composed of 4 GPUs and 2 multicores show that we are able to reduce the execution time with hybrid platforms, when compared to the GPU-only solution. We also show that our workload adjustment technique can provide signi cant performance gains in our target platform